Metrics or Mirage? An Audit of Evaluation Inconsistencies in Colonoscopy Polyp Segmentation Benchmarks

Progress in colonoscopy polyp segmentation is routinely reported through leaderboard comparisons on a small set of public benchmarks. We argue that this apparent progress is difficult to verify: a systematic audit of 27 papers published between 2015 and 2026 reveals three structural problems in how the community evaluates models. First, 25 of 27 papers textitomit the Hausdorff distance. Hausdorff distance is a boundary-accuracy metric with direct clinical relevance for detecting flat or small polyps, and is a standard in radiotherapy segmentation. Second, at least five textitincompatible train/test split protocols co-exist across papers reporting results on the same two datasets (Kvasir-SEG and CVC-ClinicDB), making published Dice scores non-comparable even when they appear in the same leaderboard column. Third, 26 of 27 papers make textitperformance claims without any statistical significance test. Strikingly, four papers published after the Metrics Reloaded framework~citemetricsreloaded2024 (Maier-Hein et al., textitNature Methods 2024) perpetuate these same problems, suggesting that general-purpose metric guidance has not yet reached the colonoscopy sub-community. To show these problems are not merely cosmetic, we re-evaluate five representative models under three controlled protocols with a single uniform scorer, and find that the reported metric conceals large boundary and recall failures, that the ``best'' model changes with the metric, and that near-tied rankings reverse across random splits. We propose a five-point Polyp Segmentation Reporting Checklist~(PSRC) as a lightweight, domain-adapted corrective.