Is the classifier confused?
The more accurate term would be "uncertain", but you are correct in your line of thought.
Your classifier (based on learned or engineered features distributions) can't determine with certainty which class to assign to this merginal cases.
It isn't necessary a case where they have a completely different distribution, more likelely that they are part of the same distribution but are located on its tail close to the tail of the distribution of the Second class (think 2 gaussians that at some area share similar density values). 

Depending on the amount of such cases, you should examine them and attempt to trace the source of confusion. Maybe some clever preprocessing could help or different features (and maybe these are just outliers).

In the multi class case, you would probably get similar behavior, but each time on a subset of all classes. I.E. the model is certain that the sample is one of 2-3 classes (out of 10 total classes for example) but it can't be certain from which specific class. So you will get probabilities of similar values for these classes and very small probabilities for the rest.