  When applying a Deep Learning model to medical images, it is crucial to
estimate the model uncertainty. Voxel-wise uncertainty is a useful visual
marker for human experts and could be used to improve the model's voxel-wise
output, such as segmentation. Moreover, uncertainty provides a solid foundation
for out-of-distribution (OOD) detection, improving the model performance on the
image-wise level. However, one of the frequent tasks in medical imaging is the
segmentation of distinct, local structures such as tumors or lesions. Here, the
structure-wise uncertainty allows more precise operations than image-wise and
more semantic-aware than voxel-wise. The way to produce uncertainty for
individual structures remains poorly explored. We propose a framework to
measure the structure-wise uncertainty and evaluate the impact of OOD data on
the model performance. Thus, we identify the best UE method to improve the
segmentation quality. The proposed framework is tested on three datasets with
the tumor segmentation task: LIDC-IDRI, LiTS, and a private one with multiple
brain metastases cases.
