  Deep neural networks (DNNs) have been deployed for many image segmentation
tasks and achieved outstanding performance. However, preparing a dataset for
training segmentation DNNs is laborious and costly since typically pixel-level
annotations are provided for each object of interest. To alleviate this issue,
one can provide only weak labels such as bounding boxes or scribbles, or less
accurate (noisy) annotations of the objects. These are significantly faster to
generate and thus result in more annotated images given the same time budget.
However, the reduction in quality might negatively affect the segmentation
performance of the resulting model. In this study, we perform a thorough
cost-effectiveness evaluation of several weak and noisy labels. We considered
11 variants of annotation strategies and 4 datasets. We conclude that the
common practice of accurately outlining the objects of interest is virtually
never the optimal approach when the annotation time is limited, even if notable
annotation time is available (10s of hours). Annotation approaches that stood
out in such scenarios were (1) contour-based annotation with rough continuous
traces, (2) polygon-based annotation with few vertices, and (3) box annotations
combined with the Segment Anything Model (SAM). In situations where unlimited
annotation time was available, precise annotations still lead to the highest
segmentation model performance.
