  The success of deep learning (DL) fostered the creation of unifying
frameworks such as tensorflow or pytorch as much as it was driven by their
creation in return. Having common building blocks facilitates the exchange of,
e.g., models or concepts and makes developments easier replicable. Nonetheless,
robust and reliable evaluation and assessment of DL models has often proven
challenging. This is at odds with their increasing safety relevance, which
recently culminated in the field of "trustworthy ML". We believe that, among
others, further unification of evaluation and safeguarding methodologies in
terms of toolkits, i.e., small and specialized framework derivatives, might
positively impact problems of trustworthiness as well as reproducibility. To
this end, we present the first survey on toolkits for uncertainty estimation
(UE) in DL, as UE forms a cornerstone in assessing model reliability. We
investigate 11 toolkits with respect to modeling and evaluation capabilities,
providing an in-depth comparison for the three most promising ones, namely
Pyro, Tensorflow Probability, and Uncertainty Quantification 360. While the
first two provide a large degree of flexibility and seamless integration into
their respective framework, the last one has the larger methodological scope.
