Ah, see, this is the perfect example of why Docker helps, especially with Python: consistency.
The team would no longer have to make these deployment decisions and argue about which tool is a better fit. They'd make the decision once, hopefully follow best Docker practices (unprivileged user, multi-stage builds, etc.), and have documentation available for how to integrate the setup with IDEs, work with volumes, etc.
Once this initial adoption hurdle is overcome, IME the productivity gains are greater than the issues of dealing with Docker. It becomes trivial to setup CI/CD, onboard new developers and integrate the app into other workflows.
Docker and containers in general have become mature enough to prove their use case and benefits, so the cargo cult argument doesn't hold weight for me.