A Decision-Aware Evaluation Protocol for Uncertainty in Medical Image Segmentation
This work introduces a decision-aware evaluation protocol for medical image segmentation uncertainty, moving beyond simple calibration scores. It separates uncertainty estimation from a deferral policy to evaluate how they combine under fixed review budgets. The protocol measures error reduction, risk coverage, and structural failure modes to determine which pixels or regions should be accepted or reviewed.