I think I got your question right (but perhaps I was not totally clear in my answer). In a nutshell: sure, there are lots of such possibly useful models that u

I think I got your question right (but perhaps I was not totally clear in my answer). In a nutshell: sure, there are lots of such possibly useful models that use trees (you can even come up with new ones yourself). Random forests or GLM regression trees are two examples. My point is that, because standard decision trees do not attempt to explicitly model probabilities (they just try to partition the data in "homogeneous groups", in some sense), you have no pre-baked assurances that your "score" will actually be well calibrated (i.e., no assurances that it will actually be a good predictor of the raw probability/confidence). Sure, you could come up with some "decision tree"-like (i.e., partitioning algorithm) Bayesian method that would (maybe) ensure reasonable calibration of "confidence"... but, at that point, are you even using "decision trees" anymore? Probably not.