it's called bias vs. variance tradeoff, or over-fitting, in stats/machine learning lingo.
I think the comment is drawing a parallel to variance (better efficiency = lower variance). Still not exactly the same, I think, but pretty damn similar.
But, erring on the side of efficiency in this discussion is more like over-fitting, which implies an overly complex model. It's making your model too good for one situation, such that it fails to generalize. You'd rather pull back on accuracy and choose a simpler model, in the hopes that it's more resilient to novel observations.