  Traditionally, segmentation algorithms require dense annotations for
training, demanding significant annotation efforts, particularly within the 3D
medical imaging field. Scribble-supervised learning emerges as a possible
solution to this challenge, promising a reduction in annotation efforts when
creating large-scale datasets. Recently, a plethora of methods for optimized
learning from scribbles have been proposed, but have so far failed to position
scribble annotation as a beneficial alternative. We relate this shortcoming to
two major issues: 1) the complex nature of many methods which deeply ties them
to the underlying segmentation model, thus preventing a migration to more
powerful state-of-the-art models as the field progresses and 2) the lack of a
systematic evaluation to validate consistent performance across the broader
medical domain, resulting in a lack of trust when applying these methods to new
segmentation problems. To address these issues, we propose a comprehensive
scribble supervision benchmark consisting of seven datasets covering a diverse
set of anatomies and pathologies imaged with varying modalities. We furthermore
propose the systematic use of partial losses, i.e. losses that are only
computed on annotated voxels. Contrary to most existing methods, these losses
can be seamlessly integrated into state-of-the-art segmentation methods,
enabling them to learn from scribble annotations while preserving their
original loss formulations. Our evaluation using nnU-Net reveals that while
most existing methods suffer from a lack of generalization, the proposed
approach consistently delivers state-of-the-art performance. Thanks to its
simplicity, our approach presents an embarrassingly simple yet effective
solution to the challenges of scribble supervision. Source code as well as our
extensive scribble benchmarking suite will be made publicly available upon
publication.
