I thought that each filter of a CNN produced its own channel in the output. Which means that doing dropout after each individual filter is equivalent to doing dropout after all of the filters.
The paper itself seems to support my interpretation…
From page 2 of that paper…

Our model is implemented by performing dropout after convolution layers.

This sentence appears as quoted here with no mention of any specificity in implementation of the dropout.
In section 2.2 the description of the dropout they use seems pretty standard to me.
From page 5

Implementing our Bayesian CNN is therefore as simple as using dropout after every convolution layer before pooling.

Again, no mention of any particular need to implement a custom dropout for this case.
As far as I can tell, model training is done as usual, but they say that predictions at test time should be produced using “Monte-Carlo dropout” which I haven’t yet managed to understand. But then again Bayesian stuff isn’t my thing and maybe I have understood it all backwards.