A scientific model almost always includes a very specific kind of probability: the p value. The best scientific models are also validated externally and have the results of that experiment reported -- as probabilities: false positive and false negative rates are common concepts to discuss that.
I completely agree that one can predict something that one does not believe to be the most likely outcome. But in order to even say that, you have to be implying that there is at least a partial order from "least likely" to "most likely" and that order is commonly referred to as probability.
(Edit: oh, I see where you got the "most likely" from. By convention, in a binary prediction, the prediction itself is stated in terms of the most likely outcome, with a confidence between 50 % and 100 %. It is of course possible to state the other side of the event with a confidence level < 50 % -- it's just not the way these things are usually presented.)
The very fact that this idea is expressible proves that you're combining two different ideas in your head. You're seeing the utility in making accurate predictions then, despite being perfectly capable of talking about predictions which aren't estimates, pretending that they're the same concept.
I of course don't need to appeal to your own confused words, because we live in a world where dictionaries exist.