  Recent developments in deep learning (DL) techniques have led to great
performance improvement in medical image segmentation tasks, especially with
the latest Transformer model and its variants. While labels from fusing
multi-rater manual segmentations are often employed as ideal ground truths in
DL model training, inter-rater variability due to factors such as training
bias, image noise, and extreme anatomical variability can still affect the
performance and uncertainty of the resulting algorithms. Knowledge regarding
how inter-rater variability affects the reliability of the resulting DL
algorithms, a key element in clinical deployment, can help inform better
training data construction and DL models, but has not been explored
extensively. In this paper, we measure aleatoric and epistemic uncertainties
using test-time augmentation (TTA), test-time dropout (TTD), and deep ensemble
to explore their relationship with inter-rater variability. Furthermore, we
compare UNet and TransUNet to study the impacts of Transformers on model
uncertainty with two label fusion strategies. We conduct a case study using
multi-class paraspinal muscle segmentation from T2w MRIs. Our study reveals the
interplay between inter-rater variability and uncertainties, affected by
choices of label fusion strategies and DL models.
