  Semantic segmentation is an essential component of medical image analysis
research, with recent deep learning algorithms offering out-of-the-box
applicability across diverse datasets. Despite these advancements, segmentation
failures remain a significant concern for real-world clinical applications,
necessitating reliable detection mechanisms. This paper introduces a
comprehensive benchmarking framework aimed at evaluating failure detection
methodologies within medical image segmentation. Through our analysis, we
identify the strengths and limitations of current failure detection metrics,
advocating for the risk-coverage analysis as a holistic evaluation approach.
Utilizing a collective dataset comprising five public 3D medical image
collections, we assess the efficacy of various failure detection strategies
under realistic test-time distribution shifts. Our findings highlight the
importance of pixel confidence aggregation and we observe superior performance
of the pairwise Dice score (Roy et al., 2019) between ensemble predictions,
positioning it as a simple and robust baseline for failure detection in medical
image segmentation. To promote ongoing research, we make the benchmarking
framework available to the community.
