Uncertainty in medical image segmentation tasks, especially inter-rater
variability, arising from differences in interpretations and annotations by
various experts, presents a significant challenge in achieving consistent and
reliable image segmentation. This variability not only reflects the inherent
complexity and subjective nature of medical image interpretation but also
directly impacts the development and evaluation of automated segmentation
algorithms. Accurately modeling and quantifying this variability is essential
for enhancing the robustness and clinical applicability of these algorithms. We
report the set-up and summarize the benchmark results of the Quantification of
Uncertainties in Biomedical Image Quantification Challenge (QUBIQ), which was
organized in conjunction with International Conferences on Medical Image
Computing and Computer-Assisted Intervention (MICCAI) 2020 and 2021. The
challenge focuses on the uncertainty quantification of medical image
segmentation which considers the omnipresence of inter-rater variability in
imaging datasets. The large collection of images with multi-rater annotations
features various modalities such as MRI and CT; various organs such as the
brain, prostate, kidney, and pancreas; and different image dimensions 2D-vs-3D.
A total of 24 teams submitted different solutions to the problem, combining
various baseline models, Bayesian neural networks, and ensemble model
techniques. The obtained results indicate the importance of the ensemble
models, as well as the need for further research to develop efficient 3D
methods for uncertainty quantification methods in 3D segmentation tasks.

医学图像分割中的不确定性，尤其是来自不同专家的解释和注释差异所导致的评分者之间的变异性，对于实现一致可靠的图像分割存在重大挑战。本文总结了 Quantification of Uncertainties in Biomedical Image Quantification Challenge (QUBIQ) 的基准结果，重点关注医学图像分割中评分者变异性的不确定性量化，并考虑了图像数据集中普遍存在的这种不确定性。结果表明整合模型的重要性以及发展高效的三维不确定性量化方法的进一步研究的需求。