Principle-Guided Supervision for Interpretable Uncertainty in Medical Image Segmentation

• URL: http://arxiv.org/abs/2605.10984v1
• Date: Sat, 09 May 2026 05:17:20 GMT
• Title: Principle-Guided Supervision for Interpretable Uncertainty in Medical Image Segmentation
• Abstract summary: We develop a principle-guided uncertainty supervision framework (PriUS) based on evidential learning.<n>PriUS produces more consistent uncertainty estimates while maintaining competitive segmentation performance.
• Score: 15.512833015785773
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Uncertainty quantification complements model predictions by characterizing their reliability, which is essential for high-stakes decision making such as medical image segmentation. However, most existing methods reduce uncertainty to a scalar confidence estimate, leaving its spatial distribution semantically underconstrained. In this work, we focus on uncertainty interpretability, namely, whether estimated uncertainty behaves in a human-understandable manner with respect to sources of ambiguity. We identify three perception-aligned principles requiring the spatial distribution of uncertainty to reflect: (1) image contrast between structures, (2) severity of image corruption, and (3) geometric complexity in anatomical structures. Accordingly, we develop a principle-guided uncertainty supervision framework (PriUS) based on evidential learning, in which the corresponding supervision objectives are explicitly enforced during training. We further introduce quantitative metrics to measure the consistency between predicted uncertainty and image attributes that induce ambiguity. Experiments on ACDC, ISIC, and WHS datasets showed that, compared with state-of-the-art methods, PriUS produced more consistent uncertainty estimates while maintaining competitive segmentation performance.

Related papers

• It depends: Incorporating correlations for joint aleatoric and epistemic uncertainties of high-dimensional output spaces [23.472868138533926]
  Uncertainty Quantification (UQ) plays a vital role in enhancing the reliability of deep learning model predictions.<n>This paper addresses the dual nature of uncertainty, focusing on their joint integration in high-dimensional regression tasks.<n>We propose a novel approach that approximates the resulting joint uncertainty using a low-rank plus diagonal covariance structure.
  arXiv  Detail & Related papers  (2026-08-25T13:08:17Z)
• Beyond Boundary Noise: Aggregated Aleatoric Uncertainty Fails to Capture Presence Ambiguity in 3D Lung Nodule Segmentation [13.944682026264472]
  We systematically evaluate how well aleatoric uncertainty captures presence ambiguity in 3D lung segmentation.<n>We find that entropy-based uncertainty maps align with boundary noise but carry insufficient discriminative signal for presence ambiguity.<n>A lightweight supervised ambiguity head trained on frozen segmentation features substantially outperforms entropy-aggregation-based baselines.
  arXiv  Detail & Related papers  (2026-08-14T12:12:34Z)
• Aligning LLM Uncertainty with Human Disagreement in Subjectivity Analysis [57.78407973423517]
  We advocate uncertainty-aware subjectivity analysis, where models are expected to make predictions while expressing uncertainty that reflects human disagreement.<n>To operationalize this perspective, we propose a two-phase Disagreement Perception and Uncertainty Alignment framework.<n> Experiments on three subjectivity analysis tasks show that DPUA preserves task performance while better aligning model uncertainty with human disagreement.
  arXiv  Detail & Related papers  (2026-05-11T11:52:58Z)
• Delving Aleatoric Uncertainty in Medical Image Segmentation via Vision Foundation Models [56.29123284262618]
  This work proposes leveraging the universal representation capabilities of visual foundation models to estimate inherent data uncertainty.<n>We analyze the feature diversity of the model's decoded representations and quantify their singular value energy to define the semantic perception scale for each class.<n>Based on this foundation, we design two uncertainty-driven application strategies: (1) the aleatoric uncertainty-aware data filtering mechanism to eliminate potentially noisy samples and enhance model learning quality; and (2) the dynamic uncertainty-aware optimization strategy that adaptively adjusts class-specific loss weights during training based on the semantic perception scale, combined with a label denoising mechanism to improve training stability
  arXiv  Detail & Related papers  (2026-04-13T03:59:54Z)
• Heterogeneous Uncertainty-Guided Composed Image Retrieval with Fine-Grained Probabilistic Learning [49.28548464288051]
  Composed Image Retrieval (CIR) enables image search by combining a reference image with modification text.<n>In intrinsic noise in CIR triplets incurs intrinsic uncertainty and threatens the model's robustness.<n>This paper introduces a Heterogeneous Uncertainty-Guided (HUG) paradigm to overcome these limitations.
  arXiv  Detail & Related papers  (2026-01-16T16:05:49Z)
• Uncertainty-Supervised Interpretable and Robust Evidential Segmentation [16.64288013782432]
  Uncertainty estimation has been widely studied in medical image segmentation as a tool to provide reliability.<n>Previous methods generally lack effective supervision in uncertainty estimation.<n>We propose a self-supervised approach to guide the learning of uncertainty.
  arXiv  Detail & Related papers  (2025-09-21T14:31:47Z)
• SConU: Selective Conformal Uncertainty in Large Language Models [59.25881667640868]
  We propose a novel approach termed Selective Conformal Uncertainty (SConU)<n>We develop two conformal p-values that are instrumental in determining whether a given sample deviates from the uncertainty distribution of the calibration set at a specific manageable risk level.<n>Our approach not only facilitates rigorous management of miscoverage rates across both single-domain and interdisciplinary contexts, but also enhances the efficiency of predictions.
  arXiv  Detail & Related papers  (2025-04-19T03:01:45Z)
• Introspective Deep Metric Learning [91.47907685364036]
  We propose an introspective deep metric learning framework for uncertainty-aware comparisons of images. The proposed IDML framework improves the performance of deep metric learning through uncertainty modeling.
  arXiv  Detail & Related papers  (2023-09-11T16:21:13Z)
• Principal Uncertainty Quantification with Spatial Correlation for Image Restoration Problems [35.46703074728443]
  PUQ -- Principal Uncertainty Quantification -- is a novel definition and corresponding analysis of uncertainty regions. We derive uncertainty intervals around principal components of the empirical posterior distribution, forming an ambiguity region. Our approach is verified through experiments on image colorization, super-resolution, and inpainting.
  arXiv  Detail & Related papers  (2023-05-17T11:08:13Z)
• Towards Reliable Medical Image Segmentation by Modeling Evidential Calibrated Uncertainty [57.023423137202485]
  Concerns regarding the reliability of medical image segmentation persist among clinicians.<n>We introduce DEviS, an easily implementable foundational model that seamlessly integrates into various medical image segmentation networks.<n>By leveraging subjective logic theory, we explicitly model probability and uncertainty for medical image segmentation.
  arXiv  Detail & Related papers  (2023-01-01T05:02:46Z)
• CRISP - Reliable Uncertainty Estimation for Medical Image Segmentation [6.197149831796131]
  We propose CRISP a ContRastive Image for uncertainty Prediction method. At its core, CRISP implements a contrastive method to learn a joint latent space which encodes a distribution of valid segmentations. We use this joint latent space to compare predictions to thousands of latent vectors and provide anatomically consistent uncertainty maps.
  arXiv  Detail & Related papers  (2022-06-15T16:56:58Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.