Let's say I have a model which has a binary classification task (Two classes of 0 and 1) and therefore, it outputs a number between 0 and 1, if it is greater than 0.5 we consider it to be class 1 and 0 the other way around.
Now let's say we remove any results in the test set that its output is between two thresholds of 0.4 and 0.6 to make the model more confident. To be more clear, if the output is in that bound, the model just prints "I'm not confident about this image".
Is this approach a good idea in general?
What if the task is about a binary classification of a medical dataset like COVID?
And if so, has this approach used in any recent research?