  Image annotation is one essential prior step to enable data-driven
algorithms. In medical imaging, having large and reliably annotated data sets
is crucial to recognize various diseases robustly. However, annotator
performance varies immensely, thus impacts model training. Therefore, often
multiple annotators should be employed, which is however expensive and
resource-intensive. Hence, it is desirable that users should annotate unseen
data and have an automated system to unobtrusively rate their performance
during this process. We examine such a system based on whole slide images
(WSIs) showing lung fluid cells. We evaluate two methods the generation of
synthetic individual cell images: conditional Generative Adversarial Networks
and Diffusion Models (DM). For qualitative and quantitative evaluation, we
conduct a user study to highlight the suitability of generated cells. Users
could not detect 52.12% of generated images by DM proofing the feasibility to
replace the original cells with synthetic cells without being noticed.
