Rethinking Uncertainty Quantification and Entanglement in Image Segmentation
The study investigated how aleatoric (data-driven) and epistemic (model-driven) uncertainty interact in medical image segmentation. It proposed a metric to measure this entanglement, finding that ensembles consistently show better performance and lower entanglement across various tasks like OOD detection and calibration.