Rethinking Uncertainty in Segmentation: From Estimation to Decision

• URL: http://arxiv.org/abs/2604.13262v1
• Date: Tue, 14 Apr 2026 19:52:05 GMT
• Title: Rethinking Uncertainty in Segmentation: From Estimation to Decision
• Abstract summary: In medical image segmentation, uncertainty estimates are often reported but rarely used to guide decisions.<n>We formulate segmentation as a two-stage pipeline, estimation followed by decision, and show that optimizing uncertainty alone fails to capture most of the achievable safety gains.<n>Our results show that the best method and policy combination removes up to 80 percent of segmentation errors at only 25 percent pixel deferral.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In medical image segmentation, uncertainty estimates are often reported but rarely used to guide decisions. We study the missing step: how uncertainty maps are converted into actionable policies such as accepting, flagging, or deferring predictions. We formulate segmentation as a two-stage pipeline, estimation followed by decision, and show that optimizing uncertainty alone fails to capture most of the achievable safety gains. Using retinal vessel segmentation benchmarks (DRIVE, STARE, CHASE_DB1), we evaluate two uncertainty sources (Monte Carlo Dropout and Test-Time Augmentation) combined with three deferral strategies, and introduce a simple confidence-aware deferral rule that prioritizes uncertain and low-confidence predictions. Our results show that the best method and policy combination removes up to 80 percent of segmentation errors at only 25 percent pixel deferral, while achieving strong cross-dataset robustness. We further show that calibration improvements do not translate to better decision quality, highlighting a disconnect between standard uncertainty metrics and real-world utility. These findings suggest that uncertainty should be evaluated based on the decisions it enables, rather than in isolation.

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