Dropouts in general are used in conjunction with fully connected layers. My understanding is that BatchNorm along with helping the model to converge faster, also acts as a regularizer(which serves the purpose).
It ‘could’ be derived from experimental evidence that dropouts may not be well suited for convolutions. That said, there is nothing stopping you in using batchnorm and dropout together.