I disagree although I don't have any real literature to quote in either direction.
From a practical perspective, it would be impossible for me to show a model to someone and say
"we think that the event is really an event if its corresponding class probability is greater than 2.5%."
The class probability distribution tend to pathological in this way when there is a moderate imbalance or worse. I've found that rebalancing solves that issue.
Now, it would be reasonable to say "well, if most of the data is not an event, the posterior probability should look this way" and, in a sense, that is completely correct. However, it doesn't make for a good predictive model because the likelihood will have to be extraordinarily powerful to overcome the issue of an extreme prior.
My former boss and I had this discussion all of the time because he would only view the model results from a very strict Bayesian point of view (and would not even want me to work on those projects). There is a difference between a good predictive class probability and a strict posterior that is consistent with the underlying event rate (since the latter may not ever be useful).
I'm not right (nor am I wrong); it is a matter of what you are trying to attain.