You can't make PA complete by adding new axioms with a deterministic algorithm. But what if you used a randomized algorithm? Richard Lipton wrote a post about this idea: generate a random bitstring S, then add to PA the axiom that the K-complexity of S is high enough. That is probabilistically very likely to be true, but is always unprovable in PA for long enough S. Clearly this gives you a stronger theory, but how much stronger? In particular, is there any hope at all that you can approach completeness in some suitable probabilistic sense?
Nah, don't get your hopes up. In the comments to Lipton's post, Alexander Shen (amusingly, one of my former schoolteachers) and Harvey Friedman show that most true statements remain unreachable by this procedure. Leonid Levin proved a weaker but more general result, roughly saying that randomized algorithms cannot complete PA with positive probability.
So the idea doesn't seem to work. But it was a very nice try.
Well, not if you want to proof mathematical theorems.
Still, in some cases it can be useful to trait absolute certainty for only a probabilistic certainty (think bloom filters); and for some game-theoretical strategies it can be beneficial to add actual randomness.
Indeed. For me, cryptographic hashing is the most salient example of this. Software like git builds entire castles on the probabilistic certainty that SHA-1 hash collisions never happen.