  Lesions or organ boundaries visible through medical imaging data are often
ambiguous, thus resulting in significant variations in multi-reader
delineations, i.e., the source of aleatoric uncertainty. In particular,
quantifying the inter-observer variability of manual annotations with Magnetic
Resonance (MR) Imaging data plays a crucial role in establishing a reference
standard for various diagnosis and treatment tasks. Most segmentation methods,
however, simply model a mapping from an image to its single segmentation map
and do not take the disagreement of annotators into consideration. In order to
account for inter-observer variability, without sacrificing accuracy, we
propose a novel variational inference framework to model the distribution of
plausible segmentation maps, given a specific MR image, which explicitly
represents the multi-reader variability. Specifically, we resort to a latent
vector to encode the multi-reader variability and counteract the inherent
information loss in the imaging data. Then, we apply a variational autoencoder
network and optimize its evidence lower bound (ELBO) to efficiently approximate
the distribution of the segmentation map, given an MR image. Experimental
results, carried out with the QUBIQ brain growth MRI segmentation datasets with
seven annotators, demonstrate the effectiveness of our approach.
