Take a look at this paper by Cosma Shalizi and Andrew Gelman about philosophy and Bayesianism. Gelman is a proeminent bayesian and Shalizi a frequentist!
Take a look also at this short criticism by Shalizi, where he points the necessity of model checking and mock the dutch book argument used by some Bayesians.
And last, but not least, I think that, since you are a physicist, you may like this text, where the author points to “computational learning theory” (which I frankly know nothing at all), which could be an alternative to Bayesianism, as far as I can understand it (not much).
ps.: If you follow the links, specially the last one and have an opinion about the text (and the discussions that followed the text at the blog of the author)
ps.2: My own take on this: Forget about the issue of objective vs subjective probability, the likelihood principle and the argument about the necessity of being coherent. Bayesian methods are good when they allow you to model your problem well (for instance, using a prior to induce unimodal posterior when there is a bimodal likelihood etc.) and the same is true for frequentist methods. Also, forget about the stuff about the problems with p-value. I mean, p-value sucks, but in the end they are a measure of uncertainty, in the spirit of how Fisher thought of it.