What machine learning accuracy is assumed to be predictable for TRNG/PUF application? In regular machine learning (ML) applications, usually an accuracy of greater than 95% is desired. 

In an ideal TRNG/PUF applications, unpredictable behavior (50% accuracy with ML models) is desired. How do we define the predictability in these applications? For example, is 60% predictable TRNG/PUF still a good one? 
If it depends on the application, it will be so helpful if application examples  with their features can be named.
No we still idealistically target 50% inter hamming distance for PUFs, but being probabilistic and cumulative it's not set in stone.  It's just that you achieve 100% material efficiency at 50%. Or you can use the Jaccard index which should tend to zero, as in the variance of sets A and B  :-



I can't find many academic papers that claim a successful PUF implementations at a 60%/40% hamming distance or 0.5 Jaccard index.  It's even worse commercially, as technology like Maxim's ChipDNA is mired in patents and industrial secrecy.  Maxim only distributes it's security user guide for those chips on request (and I didn't).

That said, the following table lists some   tests for differing PUF constructs. Be aware though that they are not real.  They're based on Monte Carlo simulations which obviously requires pseudo random inputs.  The Flip-Flop PUF seems inefficient.  The bottom entry for the TV-PUF is what the authors are proposing, at ~50%.  



From: Saha, Sehwag, V-PUF:A Fast Lightweight Analog Physical Unclonable Function. Paper.

Remember that the fuzzy extractor can do a lot with these values.  Given commercial confidentiality, it's quite hard to get to the bottom of what the physical PUF can do, and what the helper data and it's cunning algorithm glosses over.  It's easy create an extractor that will accept 128-bit input values and guarantees that challenges within 8 bits of each other will produce the same output with over 0.9999 probability .

Depending on your opinion of eprint.iacr.org, here's a DRAM PUF from Carnegie Mellon University which gives a Jaccard index &lt; 0.25 (success). So as I opened, it's not set in stone at 50% hamming or 0 Jaccard.  You can always leverage the helper data given a large enough non-deterministic component.



It's worth noting that TRNGs and PUFs are kinda opposites. A PUF is ultimately required to be entirely deterministic, whilst a TRNG needs exactly the opposite behaviour. Non reproducibility is not   the same as non determinism.   Although it's not impossible, one construction tends not to be used for the other as that's not very efficient.