I currently believe that the geometric mean of odds should be the default option for aggregating forecasts. In the two large scale empirical evaluations I am aware of [1][2], it surpasses the mean of probabilities and the median (*). It is also the only method that makes the group aggregate behave as a Bayesian, and (in my opinion) it behaves well with extreme predictions.
If you are not aggregating all-considered views of experts, but rather aggregating models with mutually exclusive assumptions, use the mean of probabilities.
Jaime Seville (who usually argues in favor of using geometric mean of odds over arithmetic mean of probabilities) makes a similar point here: