Not true. Maximum likelihood picks the hypothesis/theory that best "explains" the data, hence you run into issues with overfitting, etc. A Bayesian starts with a prior over a set of hypotheses (the P(T) term), and then uses the data to update their confidence in the various hypotheses. Assuming you have a sane prior, you end up with a simple theory with a record of making decent predictions (i.e. the kind of thing you look for in science, cf. Occam's razor, etc).

I don't see any conflict with the scientific method (at least in a fundamental sense). Scientists aren't oracles - hypotheses need to come from somewhere. Some call it intuition - I would call it an implicit application of Bayesian reasoning, where the data is experience, and the prior is governed by genetic constraints of the brain. From this you obtain a set of intuitive hypotheses to be (further) tested (i.e. those with large posterior P(T|O)).

Testing a set of hypotheses then just involves collecting observations that differentiate them (i.e. where they make conflicting predictions). This can still be considered an application of Bayes' rule, but usually one tries to collect enough data that it's quite obvious which is consistently making the best predictions (in other words has posterior close to 1), in which case it becomes a theory.