  Medical image segmentation is a challenging task, particularly due to inter-
and intra-observer variability, even between medical experts. In this paper, we
propose a novel model, called Probabilistic Inter-Observer and iNtra-Observer
variation NetwOrk (Pionono). It captures the labeling behavior of each rater
with a multidimensional probability distribution and integrates this
information with the feature maps of the image to produce probabilistic
segmentation predictions. The model is optimized by variational inference and
can be trained end-to-end. It outperforms state-of-the-art models such as
STAPLE, Probabilistic U-Net, and models based on confusion matrices.
Additionally, Pionono predicts multiple coherent segmentation maps that mimic
the rater's expert opinion, which provides additional valuable information for
the diagnostic process. Experiments on real-world cancer segmentation datasets
demonstrate the high accuracy and efficiency of Pionono, making it a powerful
tool for medical image analysis.
