arXiv:2605.18329v2 Announce Type: replace-cross Abstract: Ensemble disagreement is widely used as a proxy for epistemic uncertainty in medical image segmentation. In practice, many studies form ensembles via K-fold cross-validation (CV), yet refer to them as ``deep ensembles'' (DE). Because CV members are trained on different data subsets, their disagreement mixes seed-driven variability with data-exposure effects, which can change how uncertainty should be interpreted. We audit recent segmentation uncertainty studies and find that terminology--implementation mismatches are common. We then com
This research highlights a growing need for precision and rigorous methodology in AI research, particularly as models become more integrated into critical applications like medical imaging.
The misinterpretation of uncertainty in AI models, especially those used in sensitive areas like medical diagnoses, can lead to incorrect decisions and erode trust in AI systems.
This paper challenges current common practices in AI uncertainty estimation, suggesting that what is often labeled as 'deep ensemble' is not, necessitating a re-evaluation of how such systems are constructed and interpreted.
- · AI researchers prioritizing rigorous methodology
- · Healthcare providers adopting AI
- · Patients benefiting from more accurate AI diagnoses
- · AI developers using imprecise terminology
- · Medical AI models with unverified uncertainty estimates
Increased scrutiny and refinement of AI uncertainty quantification methods will become standard practice in research and development.
New benchmarks and best practices will emerge for evaluating and reporting epistemic uncertainty in AI models, especially in high-stakes applications.
This could lead to a bifurcation in AI development, with a premium placed on transparent and certifiable uncertainty for safety-critical systems while other applications tolerate more heuristic approaches.
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