医学影像分割中，不确定性的决策转化比估计更重要。
Rethinking Uncertainty in Segmentation: From Estimation to Decision
- 将分割分为估计算和决策两阶段，优化决策策略提升安全性
- 仅25%像素弃用即可消除80%错误，跨数据集表现稳健
- 现有校准指标不等于实际决策效果，需以决策结果评估不确定性
在医学图像分割中，不确定性估计常被报告却少用于指导决策。本文研究缺失的环节：如何将不确定性图转化为可执行的策略（如接受、标记或推迟预测）。将分割建模为两阶段流程——估计与决策，并表明仅优化不确定性无法捕捉大部分可实现的安全收益。基于视网膜血管分割基准（DRIVE、STARE、CHASE_DB1），评估了蒙特卡洛丢弃与测试时增强两种不确定性来源，结合三种弃用策略，提出一种简单且关注置信度的弃用规则，优先处理不确定且低置信的预测。结果表明，最优方法与策略组合可在仅25%像素弃用的情况下，消除高达80%的分割错误，且具备强跨数据集鲁棒性。进一步发现，校准改进并不带来更好决策质量，揭示标准不确定性指标与真实应用价值间的脱节。研究强调，应基于不确定性所支持的决策效果来评估其有效性，而非孤立看待。
原文摘要 · Abstract (English)
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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