  Among the various options to estimate uncertainty in deep neural networks,
Monte-Carlo dropout is widely popular for its simplicity and effectiveness.
However the quality of the uncertainty estimated through this method varies and
choices in architecture design and in training procedures have to be carefully
considered and tested to obtain satisfactory results. In this paper we present
a study offering a different point of view on the behavior of Monte-Carlo
dropout, which enables us to observe a few interesting properties of the
technique to keep in mind when considering its use for uncertainty estimation.
