Branch Prediction and Random Processes (e.g. Monte Carlo)

I came across a post on branch prediction (Why is processing a sorted array faster than an unsorted array?) and it got me thinking about my own Monte Carlo simulations. For the sake of specificity, let's say we are working in C and writing for CPUs. (Does that matter?) Suppose you have a loop that goes something like this:

while ( condition ) {

    // dE will be stochastic...
    dE = MonteCarloMove();

    // ...and d is a random number between 0 and 1
    d = RandomRealNumber(0,1);

    w = exp(-dE);
    if ( dE < 0 || d < w ) {
        AcceptMonteCarloMove();
    }
    else {
        RejectMonteCarloMove();
    }
}

It seems to me that branch prediction at the if..else statement is completely useless, since it is random which way we go. In a properly designed Monte Carlo simulation, you'll have roughly as many accepts as rejections. So here are my questions:

1. Is there any technique one could use to avoid the branching slowdown in a random process like this?
2. If so, is it likely to provide any significant speed-up, considering that MonteCarloMove() probably involves some expensive numerical calculations?