It makes sense to me intuitively (though I'm not sure if my reasoning is actually correct).

Worse model may not "know" enough to distinguish between a 70 and a 100 candidate, so it's expected that it's output has high variance. But a better model might "know" enough, so it can be more confident and thus more consistent.