  Accurate retinal vessel (RV) segmentation is a crucial step in the
quantitative assessment of retinal vasculature, which is needed for the early
detection of retinal diseases and other conditions. Numerous studies have been
conducted to tackle the problem of segmenting vessels automatically using a
pixel-wise classification approach. The common practice of creating ground
truth labels is to categorize pixels as foreground and background. This
approach is, however, biased, and it ignores the uncertainty of a human
annotator when it comes to annotating e.g. thin vessels. In this work, we
propose a simple and effective method that casts the RV segmentation task as an
image-level regression. For this purpose, we first introduce a novel
Segmentation Annotation Uncertainty-Aware (SAUNA) transform, which adds pixel
uncertainty to the ground truth using the pixel's closeness to the annotation
boundary and vessel thickness. To train our model with soft labels, we
generalize the earlier proposed Jaccard metric loss to arbitrary hypercubes for
soft Jaccard index (Intersection-over-Union) optimization. Additionally, we
employ a stable version of the Focal-L1 loss for pixel-wise regression. We
conduct thorough experiments and compare our method to a diverse set of
baselines across 5 retinal image datasets. Our empirical results indicate that
the integration of the SAUNA transform and these segmentation losses led to
significant performance boosts for different segmentation models. Particularly,
our methodology enables UNet-like architectures to substantially outperform
computational-intensive baselines. Our implementation is available at
\url{https://github.com/Oulu-IMEDS/SAUNA}.
