Selecting models for ensemble from large group of models with high uncertaintly
The general principle of ensemble learning is indeed to rely on the diversity of the individual learners rather than their performance. Therefore it's ok to include models which perform well by chance (typically because they're overfit), since if there is sufficient diversity across the models it's very unlikely that two models would be overfit in the same way, i.e. that they would wrongly predict the same instances by chance. So if most models perform reasonably well in general through different mechanisms, it is expected that for any given instance most models will predict the right answer and only a few will be wrong.
The risk of using models which have a quite high correlation together is to defeat the purpose of ensemble learning and obtain a performance similar to the best individual learner.
Note also that the risk of overfitting is quite low if the individual learners have been tested with CV. It could also be worth checking the variance of the performance across CV runs (high variance indicating potential instability), but even that is not so useful in my experience: it's really the diversity of the learners which makes ensemble learning work optimally.
But as usual a lot depends on the actual data/task, so I'd suggest properly testing a few different approaches.