random forest classifier - impact of small n_estimator and repeated training
there is a parameter in estimators that is called random_state that fixes a seed for your algorithm run and reruns exactly the same so you can expect the same results at every rerun of the exact same code. It makes your code deterministic. But yes, in random forests in particular, due to their nature of averaging across all trees created when the forest is grown, variance in your result becomes less evident. So your reasoning is correct.

If you want to make your low_estimators RF produce the same results at every run, just add random_state and give it a random number