Learning a medical image segmentation model is an inherently ambiguous task,
as uncertainties exist in both images (noise) and manual annotations (human
errors and bias) used for model training. To build a trustworthy image
segmentation model, it is important to not just evaluate its performance but
also estimate the uncertainty of the model prediction. Most state-of-the-art
image segmentation networks adopt a hierarchical encoder architecture,
extracting image features at multiple resolution levels from fine to coarse. In
this work, we leverage this hierarchical image representation and propose a
simple yet effective method for estimating uncertainties at multiple levels.
The multi-level uncertainties are modelled via the skip-connection module and
then sampled to generate an uncertainty map for the predicted image
segmentation. We demonstrate that a deep learning segmentation network such as
U-net, when implemented with such hierarchical uncertainty estimation module,
can achieve a high segmentation performance, while at the same time provide
meaningful uncertainty maps that can be used for out-of-distribution detection.

建立可信赖的图像分割模型既需要评估其性能又需要估计模型预测的不确定性。本研究提出了一种利用多层不确定性模块估计图像分割的简单有效方法，并证明了采用此方法实现的深度学习分割网络能够同时实现高分割性能和有意义的不确定性图，可用于识别分布之外的数据。